Understanding Decoder-Only Transformers Part 2: Decoder-Only vs Regular Transformers

In this article, we will look at the differences between a decoder-only transformer and a standard (encoder–decoder) transformer.

How Decoder-Only Transformers Work

A decoder-only transformer uses the same components to process the input prompt and to generate the output.

It relies on masked self-attention, which considers only the current word and the words that came before it.

Masked self-attention is applied to both:

• the input prompt
• the generated output

This means the entire process is handled by a single stack of decoder layers.

How Regular Transformers Work

A regular transformer has two separate parts:

• an encoder to process the input
• a decoder to generate the output

When encoding the input, it uses self-attention, not masked self-attention.
This allows each word to attend to all other words in the input, not just the previous ones.

The decoder then uses encoder–decoder attention to stay connected to the input.

In this mechanism:

• queries come from the decoder
• keys and values come from the encoder

This helps the decoder focus on the most important parts of the input while generating output.

What Really Changes Between Them

• Decoder-only transformers use masked self-attention everywhere (for both input and output)

• Standard transformers use:

  • self-attention in the encoder
  • masked self-attention in the decoder
  • encoder–decoder attention to connect input and output

That wraps up decoder-only transformers.

In the next article, we will explore encoder-only transformers.

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